Create a data management plan

Describes the management of the research data throughout the project

A data management plan is a document that describes how research data that is used for research within a project will be securely and consistently managed throughout that project. A data management plan is drawn up by the project PI, but it is of great benefit to all members of the project as it serves as a shared reference point which everyone can refer to.

A data management plan creates a common understanding and agreement on things such as file formats, standards, storage areas and how you name your files during the project. Having a clear plan makes it easier for the project PI to introduce guidelines to both new and existing project members and helps members to know what to do when handling the project's research data.  

An active working document  

A data management plan should be regarded as a working document that changes and is updated as data is collected and managed and as the project progresses. For example, some funders require that a data management plan is in place at the start of the project and that there is a plan for updating it at specific points or phases in the project. The data management plan should be up to date and current, especially if the project has several partners and the data collection is carried out by more than one person.

Create a data management plan in the DMPOnline tool

Different funders have different requirements, guidelines and templates for what should be included in a data management plan.

At the University, you are offered use of the DMPOnline tool where you can create and manage your data management plans. You can choose to use Lund University's template, which covers many of the funders' requirements, but you will also find several templates from funders in the tool.  

What a data management plan might include  

Whether you use the University's template or a template from a specific research funder, you should remember that the fields you fill in when working in the tool are meant to be updated and developed during the project. For example, you may initially only give a rough estimate of the types of files you expect to collect, but as the project progresses, the information in the plan will be updated and become more specific.  

Data description

Under this heading you can describe how and in what form data will be collected. You can also describe the method and the file formats you will save your data in.  

Documentation and data quality  

Here you can describe how your data will be documented in the project, for example via metadata, content, structure, standards and formats to enable you and other researchers or computer programs to read and interpret your data correctly.  

Storage and backup  

Here you can describe things related to where data is stored and document its access in relation to security classification. It is also helpful if you document how you are going to ensure the integrity of the storage and backup of data and metadata during the research process.  

Legal and ethical requirements  

Here you describe how data is processed in accordance with rules and legislation. It is particularly important that this is documented when you are working with sensitive data or data that warrants special protection. You may wish to refer to the relevant documents and agreements.

Data sharing and long-term storage  

Describe if and how data will be shared. If there are legal or ethical barriers to sharing, document them. Even if data cannot be shared, there should be a plan for its long-term preservation and longevity.

Responsibility and resources

This section describes the division of responsibilities during the project.  

Log in to DMPOnline  

Use your data management plan as a basis for documentation

The data management plan can serve as a basis for creating a so-called README file that summarises information about the dataset's content, structure, metadata and security aspects. The README file becomes a practical document that provides an overview and makes it easier for others to understand and use the dataset.

Document your research data in a README file